Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia.

BACKGROUND: Artificial intelligence (AI) is a novel technology that has been widely acknowledged for its potential to improve the processes' efficiency across industries. However, its barriers and facilitators in healthcare are not completely understood due to its novel nature. STUDY PURPOSE: The purpose of this study is to explore the intricate landscape of AI use in family medicine, aiming to uncover the factors that either hinder or enable its successful adoption. METHODS: A cross-sectional survey design is adopted in this study. The questionnaire included 10 factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, personal innovativeness, ethical concerns, and facilitators) affecting the acceptance of AI. A total of 157 family physicians participated in the online survey. RESULTS: Effort expectancy (µ = 3.85) and facilitating conditions (µ = 3.77) were identified to be strong influence factors. Access to data (µ = 4.33), increased computing power (µ = 3.92), and telemedicine (µ = 3.78) were identified as major facilitators; regulatory support (µ = 2.29) and interoperability standards (µ = 2.71) were identified as barriers along with privacy and ethical concerns. Younger individuals tend to have more positive attitudes and expectations toward AI-enabled assistants compared to older participants (p < .05). Perceived privacy risk is negatively correlated with all factors. CONCLUSION: Although there are various barriers and concerns regarding the use of AI in healthcare, the preference for AI use in healthcare, especially family medicine, is increasing.

AI-Powered Mental Health Virtual Assistants' Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z.

STUDY PURPOSE: This study aims to analyze various influencing factors among generations X (Gen X), Y (Gen Y), and Z (Gen Z) of artificial intelligence (AI)-powered mental health virtual assistants. METHODS: A cross-sectional survey design was adopted in this study. The study sample consisted of outpatients diagnosed with various mental health illnesses, such as anxiety, depression, schizophrenia, and behavioral disorders. A survey questionnaire was designed based on the factors (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention) identified from the unified theory of acceptance and use of the technology model. Ethical approval was received from the Ethics Committee at Imam Abdulrahman Bin Faisal University, Saudi Arabia. RESULTS: A total of 506 patients participated in the study, with over 80% having moderate to high experience in using mental health AI assistants. The ANOVA results for performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioral intentions (BI) indicate that there are statistically significant differences (p < 0.05) between the Gen X, Gen Y, and Gen Z participants. CONCLUSION: The findings underscore the significance of considering generational differences in attitudes and perceptions, with Gen Y and Gen Z demonstrating more positive attitudes and stronger intentions to use AI mental health virtual assistants, while Gen X appears to be more cautious.

Surveying Hematologists' Perceptions and Readiness to Embrace Artificial Intelligence in Diagnosis and Treatment Decision-Making.

AIM: This study aims to explore the critical dimension of assessing the perceptions and readiness of hematologists to embrace artificial intelligence (AI) technologies in their diagnostic and treatment decision-making processes. METHODS: This study used a cross-sectional design for collecting data related to the perceptions and readiness of hematologists using a validated online questionnaire-based survey. Both hematologists (MD) and postgraduate MD students in hematology were included in the study. A total of 188 participants, including 35 hematologists (MD) and 153 MD hematology students, completed the survey. RESULTS: Major challenges include "AI's level of autonomy" and "the complexity in the field of medicine." Major barriers and risks identified include "lack of trust," "management's level of understanding," "dehumanization of healthcare," and "reduction in physicians' skills." Statistically significant differences in perceptions of benefits including resources (p=0.0326, p<0.05) and knowledge (p=0.0262, p<0.05) were observed between genders. Older physicians were observed to be more concerned about the use of AI compared to younger physicians (p<0.05). CONCLUSION: While AI use in hematology diagnosis and treatment decision-making is positively perceived, issues such as lack of trust, transparency, regulations, and poor AI awareness can affect the adoption of AI.

The Impact of Social Media Applications on Donor Engagement and Retention in the Saudi Arabian Blood Donation System.

STUDY PURPOSE: The objective of this study is to evaluate the influence of social media applications on donor engagement and retention within the blood donation system in Saudi Arabia. METHODS: A cross-sectional survey design was adopted in this study. The population aged above 18 years and living in Saudi Arabia was included in the study. Using convenience and snowball sampling techniques, an online questionnaire was distributed using social media channels such as WhatsApp, Facebook, and Instagram. A total of 463 participants were included in the study. RESULTS: The majority of the study participants (78.1%) engage on social media applications multiple times a day for charity causes such as blood donation by responding to requests, while 51.8% of them engage on social media applications for the same reason a few times a month. Focusing on donor engagement, 46.8% and 27.3% of the total participants were likely to engage in the blood donation process; 60% were likely to continue to use social media applications for blood donation. The ANOVA findings showed a significant difference (p<0.05) between participant groups characterized by age and educational level on their engagement on social media applications for the blood donation process. Younger participants and participants with bachelor's degrees and above were more likely to engage in social media applications for the blood donation process compared to minimum educated and older participants (p<0.05). CONCLUSION: Charity or blood donation organizations must adopt strategies to actively engage the donors on the platforms, as social media can effectively contribute to donor engagement and retention.

Use of social media for the improvement of safety knowledge and awareness among Saudi Arabian phlebotomists.

Purpose: The purpose of this study is to investigate the use of social media for the improvement of safety knowledge and awareness among phlebotomists. Methods: As this study was intended to arrive at specific conclusions using empirical evidence, a deductive quantitative cross-sectional online survey design was adopted. A total of 521 phlebotomists participated in the survey, and 86 incomplete responders were removed, resulting in a final sample of 435 considered in this study. T-tests and ANOVA were used to analyze the data. Results: A total of 41.6% stated that social media was very effective, and 31.5% stated that it was somewhat effective in improving safety knowledge and awareness. in addition, this study revealed no major differences between male and female participants (p > 0.05) with respect to the effectiveness of social media. However, statistically significant differences (p < 0.05) among the age groups were identified in relation to the effectiveness of social media and the intention to use it in the future. Conclusion: Social media applications are effective for knowledge dissemination among healthcare professionals.

Gd3+-substituted BiFeO3 perovskite nanoparticles: facile synthesis, characterization, and applications in heterogeneous catalysis.

We present the combustion-based synthesis of BiFeO3 (BFO) and Gd:BiFeO3 perovskite nanoparticles. XRD analysis demonstrates that the undoped BFO (x = 0) perovskite sample shows a single perovskite phase with a rhombohedral structure. However, increase in the Gd3+ content from x = 0.05 and 0.15 to 0.25 led to the occurrence of a structural phase transformation from rhombohedral (BiFeO3) to orthorhombic (Bi2Fe4O9). With an increase in the Gd-dopant the average crystallite size of rhombohedral structures increased from 16 to 23 nm. The perovskite samples were examined using XPS, which confirmed the presence of Bi3+, Gd3+, Fe2+, and O2+ ions. FT-IR spectroscopy indicated the existence of elemental functional groups in the synthesized perovskite nanoparticles. Furthermore, the direct band gap measured by DRS reduced from 2.16 to 2.0 eV as the Gd concentration increased. The nanoparticles of the BFO perovskite had an uneven shape, a tendency to agglomerate, and fused grains with defined grain boundaries. At ambient temperature, both the undoped and Gd:BFO perovskite nanoparticles exhibit a ferromagnetic characteristic. It was found that the BET surface area of the undoped and Gd-doped BFO perovskite nanoparticles varied progressively from 4.38 to 33.52 m2 g-1. The catalytic oxidation studies conducted in a batch reactor under air conditions revealed that the synthesized catalysts, in particular, Gd:BFO (x = 0.25), exhibited higher conversion and selectivity efficiencies for glycerol (con. 100% and sel. 99.5%, respectively).

Artificial Intelligence and Patient Autonomy in Obesity Treatment Decisions: An Empirical Study of the Challenges.

Background This study aims to explore the factors associated with artificial intelligence (AI) and patient autonomy in obesity treatment decision-making. Methodology A cross-sectional, online, descriptive survey design was adopted in this study. The survey instrument incorporated the Ideal Patient Autonomy Scale (IPAS) and other factors affecting patient autonomy in the AI-patient relationship. The study participants included 74 physicians, 55 dieticians, and 273 obese patients. Results Different views were expressed in the scales AI knows the best (µ = 2.95-3.15) and the patient should decide (µ = 2.95-3.16). Ethical concerns (µ = 3.24) and perceived privacy risks (µ = 3.58) were identified as having a more negative influence on patient autonomy compared to personal innovativeness (µ = 2.41) and trust (µ = 2.85). Physicians and dieticians expressed significantly higher trust in AI compared to patients (p < 0.05). Conclusions Patient autonomy in the AI-patient relationship is significantly affected by privacy, trust, and ethical issues. As trust is a multifaceted factor and AI is a novel technology in healthcare, it is essential to fully explore the various factors influencing trust and patient autonomy.

Factors Affecting the Adoption of Artificial Intelligence-Enabled Virtual Assistants for Leukemia Self-Management.

AIM AND PURPOSE: The purpose of this study is to analyze the various influencing factors affecting the adoption of artificial intelligence (AI)-enabled virtual assistants (VAs) for self-management of leukemia. METHODS: A cross-sectional survey design is adopted in this study. The questionnaire included eight factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, and personal innovativeness) affecting the acceptance of AI-enabled virtual assistants. A total of 397 leukemia patients participated in the online survey. RESULTS: Performance expectancy (µ = 3.14), effort expectancy (µ = 3.05), and personal innovativeness (µ = 3.14) were identified to be the major influencing factors of AI adoption. Statistically significant differences (p < .05) were observed between the gender-based and age groups of the participants in relation to the various factors. In addition, perceived privacy risks were negatively correlated with all other factors. CONCLUSION: Although there are negative factors such as privacy risks and ethical issues in AI adoption, perceived effectiveness and ease of use among individuals are leading to greater adoption of AI-enabled VAs.

Adoption of Wearable Insulin Biosensors for Diabetes Management: A Cross-Sectional Study.

BACKGROUND: Wearable insulin biosensors represent a novel approach that combines the benefits of real-time glucose monitoring and automated insulin delivery, potentially revolutionizing how individuals with diabetes manage their condition. STUDY PURPOSE: To analyze the behavioral intentions of wearable insulin biosensors among diabetes patients, the factors that drive or hinder their usage, and the implications for diabetes management and healthcare outcomes. METHODS: A cross-sectional survey design was adopted in this study. The validated questionnaire included 10 factors (Performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, and personal innovativeness) affecting the acceptance of wearable insulin sensors. A total of 248 diabetic patients who had used wearable sensors participated in the study. RESULTS: Performance expectancy was rated the highest (Mean = 3.84 out of 5), followed by effort expectancy (Mean = 3.78 out of 5), and trust (Mean = 3.53 out of 5). Statistically significant differences (p < 0.05) were observed with respect to socio-demographic variables including age and gender on various influencing factors and adoption intentions. PE, EE, and trust were positively associated with adoption intentions. CONCLUSION: While wearable insulin sensors are positively perceived with respect to diabetes management, issues like privacy and security may affect their adoption.

Public Awareness of Obesity as a Risk Factor for Cancer in Central Saudi Arabia: Feasibility of ChatGPT as an Educational Intervention.

BACKGROUND: While the link between obesity and chronic diseases such as diabetes and cardiovascular disorders is well-documented, there is a growing body of evidence connecting obesity with an increased risk of cancer. However, public awareness of this connection remains limited. STUDY PURPOSE: To analyze public awareness of overweight/obesity as a risk factor for cancer and analyze public perceptions on the feasibility of ChatGPT, an artificial intelligence-based conversational agent, as an educational intervention tool. METHODS: A mixed-methods approach including deductive quantitative cross-sectional approach to draw precise conclusions based on empirical evidence on public awareness of the link between obesity and cancer; and inductive qualitative approach to interpret public perceptions on using ChatGPT for creating awareness of obesity, cancer and its risk factors was used in this study. Participants included adult residents in Saudi Arabia. A total of 486 individuals and 21 individuals were included in the survey and semi-structured interviews respectively. RESULTS: About 65% of the participants are not completely aware of cancer and its risk factors. Significant differences in awareness were observed concerning age groups (p < .0001), socio-economic status (p = .041), and regional distribution (p = .0351). A total of 10 themes were analyzed from the interview data, which included four positive factors (accessibility, personalization, cost-effectiveness, anonymity and privacy, multi-language support) and five negative factors (information inaccuracy, lack of emotional intelligence, dependency and overreliance, data privacy and security, and inability to provide physical support or diagnosis). CONCLUSION: This study has underscored the potential of leveraging ChatGPT as a valuable public awareness tool for cancer in Saudi Arabia.